The present invention relates generally to uncertainty in medical images and more particularly to computation and visualization of segmentation uncertainty in medical images.
Many clinical applications nowadays rely on geometrical models for quantification of physiology and function. In a standard setting, these models are generated from two-dimension (2D), three-dimensional (3D), and 3D plus time (3D+t) medical images. Based on the estimated models, clinically relevant measurements are computed and used for diagnosis and intervention planning. For example, during transcatheter aortic valve implantation (TAVI) planning, a 3D computed tomography (CT) or transesophageal echocardiogram (TEE) image is acquired. Based on the 3D image, a patient specific surface model of the aortic valve can be extracted and distance measurements for implant sizing are derived. However, due to image noise and ill-defined boundaries caused by neighboring tissues with a similar imaging response, parts of the model boundary can be noisy, blurry, or have signal dropout. It is often challenging to correctly delineate the boundary of the anatomy in these situations. Thus, measurements derived solely from the geometric model inherit from the uncertainty of the model itself, yielding potentially sub-optimal diagnosis and planning data. In the example of TAVI, if the image at the aortic valve annulus is blurry or has signal dropout, the sizing of the implant might be sub-optimal if only the surface model is used.